class_docstr: Single-shot Object Detection Network: https://arxiv.org/abs/1512.02325.
     with resnetv1b base model.
  
  Parameters
  ----------
  network : string or None
      Name of the base network, must be end with v1b.
  add_filters : list of int
      Number of channels for the appended layers, ignored if `network`is `None`.
  norm_layer : object
      Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
      Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
      This will only apply to base networks that has `norm_layer` specified, will ignore if the
      base network (e.g. VGG) don't accept this argument.
  norm_kwargs : dict
      Additional `norm_layer` arguments, for example `num_devices=4`
      for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
  use_bn : bool
      Whether to use BatchNorm layer after each attached convolutional layer.
  reduce_ratio : float
      Channel reduce ratio (0, 1) of the transition layer.
  min_depth : int
      Minimum channels for the transition layers.
code: def hybrid_forward(self, F, x):
      y1 = self.stage1(x)
      y2 = self.stage2(y1)
      more_out = [y1, y2]
      out = y2
      for stage in self.more_stages:
          out = stage(out)
          more_out.append(out)
      return more_out
  
file_path: gluon-cv/gluoncv/model_zoo/ssd/resnet_v1b_ssd.py
imports: from __future__ import absolute_import
  import mxnet
  from mxnet.gluon import HybridBlock
  from mxnet.gluon.nn import HybridSequential, Conv2D, Activation, BatchNorm
  from ..model_zoo import get_model
label_desc: Use image node_id 2 for calling the ResNetV1bSSD obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: more_out